Traditional supervised learning methods are hitting a bottleneck [1]} because of their dependency on expensive manually labeled data and their weaknesses such as limited generalization ability and vulnerability to adversarial attacks [2]}, [3]}, [1]}. A promising
alternative, self-supervised learning,
as a type of unsupervised learning, has gained popularity because of its potential to learn effective data representations without manual labeling. Among self-supervised learning algorithms, contrastive learning has achieved state-of-the-art performance in several fields of research, including but not limited to computer vision [5]}, [6]}, [7]}, [8]}, natural language processing [9]}, [10]}, and biomedical image processing [11]}, [12]}, [13]}, [14]}.
